Machine learning uses statistical techniques for teaching computers with data to perform specific tasks without being explicitly programmed to do so. The goal of machine learning is to construct algorithms that can learn from and make predictions on data. These algorithms work by creating mathematical models which can classify data. The process of creating the models (or classifiers) can involve training and fine-tuning the model parameters using input data.
Deep learning is a machine learning technique that teaches computers to learn by example. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound, i.e., data. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. Most modern deep learning models are based on an artificial neural network.